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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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24 pages, 4302 KB  
Article
Adapted Route Instructions for Navigation Technologies in Support of Wheelchair Mobility in Urban Areas: Online Survey
by Sanaz Azimi, Mir Abolfazl Mostafavi, Krista L. Best, Aurélie Dommes and Angélique Montuwy
ISPRS Int. J. Geo-Inf. 2026, 15(3), 110; https://doi.org/10.3390/ijgi15030110 - 5 Mar 2026
Viewed by 496
Abstract
Wheelchair users face environmental barriers that limit their mobility and social participation. Although existing navigation tools support urban mobility, they often lack clear orientation and confirmation cues, and information on accessible and safe routes to meet wheelchair users’ needs. This study aims to [...] Read more.
Wheelchair users face environmental barriers that limit their mobility and social participation. Although existing navigation tools support urban mobility, they often lack clear orientation and confirmation cues, and information on accessible and safe routes to meet wheelchair users’ needs. This study aims to identify the most adapted route instructions for wheelchair users, examine characteristics’ (sociodemographic information and profiles) impact on their instructions’ choices, and evaluate instruction’s delivery modalities. An online questionnaire collected participants’ characteristics and agreement with the proposed route instruction formulations (different combinations of information like turn-by-turn instructions, landmarks, and accessibility information) regarding clarity, sufficiency, adaptability, and safety criteria. Formulations were evaluated across 14 navigation situations involving accessibility and safety challenges. Participants also rated communication modalities. 32 wheelchair-users (19 males, 13 females; mean age = 45.8 years; mean wheelchair experience = 23.5 years) participated. Data analysis reveals the importance of enriched turn-by-turn instructions, including non-turning actions, alerts, landmarks, and/or street names for participants. Alert-based formulations were favored in most situations, like uneven sidewalks, slopes and intersections. More enriched instructions were significantly acceptable among women and participants with greater wheelchair experience. Multimodal delivery, particularly visual and audio information, was also preferred. These findings help develop adaptive navigation tools, improving wheelchair users’ safe, confident mobility, autonomy, and social participation. Full article
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22 pages, 5177 KB  
Article
VGGT-Geo: Probabilistic Geometric Fusion of Visual Geometry Grounded Transformer Priors for Robust Dense Indoor SLAM
by Kai Qin, Jing Li, Sisi Zlatanova, Haitao Wu, Hao Wu, Yin Gao, Dingjie Zhou, Yuchen Li, Sizhe Shen, Xiangjun Qu, Zhenxin Zhang, Banghui Yang and Shicheng Xu
ISPRS Int. J. Geo-Inf. 2026, 15(2), 85; https://doi.org/10.3390/ijgi15020085 - 16 Feb 2026
Viewed by 1352
Abstract
With the rapid evolution of Digital Twins and Embodied AI, achieving fast, dense, and high-precision 3D perception in unknown environments has become paramount. However, existing Visual SLAM paradigms face a critical dilemma: geometry-based methods often fail in texture-less areas due to feature scarcity, [...] Read more.
With the rapid evolution of Digital Twins and Embodied AI, achieving fast, dense, and high-precision 3D perception in unknown environments has become paramount. However, existing Visual SLAM paradigms face a critical dilemma: geometry-based methods often fail in texture-less areas due to feature scarcity, while learning-based approaches frequently suffer from scale drift and unphysical deformations. To bridge this gap, we propose VGGT-Geo, a novel SLAM system that synergizes generative priors from Large Foundation Models with multi-modal geometric optimization. Distinguishing itself from simple cascaded architectures, we construct a Probabilistic Geometric Fusion framework, consisting of (1) Generative Warm-start, leveraging the holistic scene understanding capabilities of the VGGT, (2) Confidence-Aware Optimization to extract dense features via DINOv3 and predict their confidence map, and (3) a Multi-Modal Constraint Closure that fuses point-line features and metric depth priors to constrain rotational Degrees of Freedom in Manhattan Worlds. We conducted systematic evaluations on TUM, Replica, Tanks and Temples, and a challenging self-collected dataset featuring extreme lighting and texture-less walls. Experimental results demonstrate that VGGT-Geo exhibits superior robustness and accuracy in unseen environments. On our most challenging dataset, it achieves an Absolute Trajectory Error of 4–5 cm and a Relative Rotation Error of 0.79°, outperforming current state-of-the-art methods by approximately 50% in trajectory accuracy. This study validates that synergizing the intuition of Large Foundation Models with geometric rigor is a viable path toward next-generation robust SLAM. Full article
(This article belongs to the Special Issue Urban Digital Twins Empowered by AI and Dataspaces)
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19 pages, 668 KB  
Article
Analysis of Using Machine Learning Application Possibilities for the Detection and Classification of Topographic Objects
by Katarzyna Kryzia, Aleksandra Radziejowska, Justyna Adamczyk and Dominik Kryzia
ISPRS Int. J. Geo-Inf. 2026, 15(2), 59; https://doi.org/10.3390/ijgi15020059 - 27 Jan 2026
Viewed by 717
Abstract
The growing availability of spatial data from remote sensing, laser scanning (LiDAR), and photogrammetric techniques stimulates the dynamic development of methods for the automatic detection and classification of topographic objects. In recent years, both classical machine learning (ML) algorithms and deep learning (DL) [...] Read more.
The growing availability of spatial data from remote sensing, laser scanning (LiDAR), and photogrammetric techniques stimulates the dynamic development of methods for the automatic detection and classification of topographic objects. In recent years, both classical machine learning (ML) algorithms and deep learning (DL) methods have found wide application in the analysis of large and complex data sets. Despite significant achievements, literature on the subject remains scattered, and a comprehensive review that systematically compares algorithm classes with respect to data modality, performance, and application context is still needed. The aim of this article is to provide a critical analysis of the current state of research on the use of ML and DL algorithms in the detection and classification of topographic objects. The theoretical foundations of selected methods, their applications to various data sources, and the accuracy and computational requirements reported in the literature are presented. Attention is paid to comparing classical ML algorithms (including SVM, RF, KNN) with modern deep architectures (CNN, U-Net, ResNet), with respect to different data types such as satellite imagery, aerial orthophotos, and LiDAR point clouds, indicating their effectiveness in the context of cartographic and elevation data. The article also discusses the main challenges related to data availability, model interpretability, and computational costs, and points to promising directions for further research. The summary of the results shows that DL methods are frequently reported to achieve several to over ten percentage points higher segmentation and classification accuracy than classical ML approaches, depending on data type and object complexity, particularly in the analysis of raster data and LiDAR point clouds. The conclusions emphasize the practical significance of these methods for spatial planning, infrastructure monitoring, and environmental management, as well as their potential in the automation of topographic analysis. Full article
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16 pages, 11451 KB  
Article
A Spatial Statistics Methodology for Inspector Allocation Against Fare Evasion
by Susana Freiria and Nuno Sousa
ISPRS Int. J. Geo-Inf. 2026, 15(2), 53; https://doi.org/10.3390/ijgi15020053 - 24 Jan 2026
Viewed by 495
Abstract
This article discusses public transport fare evasion from the point of view of the relations between inspection actions and detected evasion, with the aim of improving the efficacy of the former. By applying spatial statistics methods to a large dataset from Lisbon, Portugal, [...] Read more.
This article discusses public transport fare evasion from the point of view of the relations between inspection actions and detected evasion, with the aim of improving the efficacy of the former. By applying spatial statistics methods to a large dataset from Lisbon, Portugal, namely, entropy-based local bivariate relationships (LBR) and geographically weighted regression (GWR), it is shown that the two variables are associated in a widespread manner throughout the city, mostly in a linear way. Mapping out marginal gains from inspection actions then shows where they detect the most evaders, allowing transport companies to relocate their inspector teams in a more effective manner. Results for Lisbon show that gains in effectiveness of circa 50% can be obtained, mostly by moving some inspector teams from the centre of the city to the periphery during daytime. The methodology requires only inspection/detection databases, which transport companies usually have, making it a valuable, practical tool to combat fare evasion. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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23 pages, 2493 KB  
Article
Rule-Based Scenario Classification Using Vehicle Trajectories
by Sungmo Ku and Jinho Lee
ISPRS Int. J. Geo-Inf. 2026, 15(1), 37; https://doi.org/10.3390/ijgi15010037 - 11 Jan 2026
Viewed by 624
Abstract
Ensuring the safety of autonomous driving systems (ADS) requires scenario-based testing that reflects the complexity and variability of real-world driving conditions. However, the nondeterministic nature of actual traffic environments makes physical testing costly and limited in scope, particularly for rare and safety-critical scenarios. [...] Read more.
Ensuring the safety of autonomous driving systems (ADS) requires scenario-based testing that reflects the complexity and variability of real-world driving conditions. However, the nondeterministic nature of actual traffic environments makes physical testing costly and limited in scope, particularly for rare and safety-critical scenarios. To address this, simulation has become a core component in validation by providing scalable, controllable, and repeatable testing environments. This study proposes a trajectory-based scenario classification framework that emphasizes both generality and interpretability. Specifically, we define a set of rule-based maneuver classification criteria using lateral acceleration patterns and apply them to simulated urban driving scenarios modeled with OpenSCENARIO. To address overlapping maneuver characteristics, a priority ordering of classification rules is introduced to resolve ambiguities. The proposed method was evaluated on a dataset comprising 7 types of maneuvers, including straight driving, lane changes, turns, roundabouts, and U-turns. Experimental results demonstrate the effectiveness of rule-driven classification based on vehicle trajectory dynamics and highlight the potential of this approach for structured scenario definition and validation in ADS simulation environments. Full article
(This article belongs to the Topic The Geography of Digital Twin: Concepts, Architectures, Modeling, AI and Applications)
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27 pages, 14285 KB  
Article
Modeling and Explaining Perceived Fear of Crime from Street View Imagery Using a GeoAI Framework
by Somang Kim, Jaeyeon Choi and Youngok Kang
ISPRS Int. J. Geo-Inf. 2026, 15(1), 18; https://doi.org/10.3390/ijgi15010018 - 31 Dec 2025
Viewed by 1088
Abstract
Understanding the spatial distribution and determinants of perceived fear of crime is essential for enhancing urban safety and promoting equitable city development. This study models and explains perceived fear of crime from street view imagery using a GeoAI framework that integrates deep learning, [...] Read more.
Understanding the spatial distribution and determinants of perceived fear of crime is essential for enhancing urban safety and promoting equitable city development. This study models and explains perceived fear of crime from street view imagery using a GeoAI framework that integrates deep learning, semantic segmentation, and explainable AI techniques. Focusing on Yeongdeungpo-gu in Seoul, South Korea—a district characterized by diverse urban morphologies—we collected 171,942 pairwise comparison responses through a large-scale crowdsourced survey designed to capture visual perceptions of crime-related fear. A Vision Transformer-based Siamese network (RSS-Swin) was employed to predict continuous fear-of-crime scores, while semantic segmentation (SegFormer-B5) and AutoML regression were applied to identify built-environment features influencing these perceptions. SHAP-based interpretability analysis was then used to quantify the importance and interactions of key visual elements. The results reveal that open and accessible streetscape components, such as roads and sidewalks, consistently mitigate perceived fear, whereas enclosed or unmanaged features, including walls, poles, and narrow alleys, heighten it. Moreover, the effects of vegetation, fences, and buildings vary across spatial contexts, emphasizing the need for place-sensitive interpretation. By integrating predictive modeling and explainable analysis, this study advances a transparent and scalable GeoAI framework for understanding the visual and environmental determinants of crime-related fear and supporting perception-aware CPTED strategies. Full article
(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)
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22 pages, 3966 KB  
Article
TAS-SLAM: A Visual SLAM System for Complex Dynamic Environments Integrating Instance-Level Motion Classification and Temporally Adaptive Super-Pixel Segmentation
by Yiming Li, Liuwei Lu, Guangming Guo, Luying Na, Xianpu Liang, Peng Su, Qi An and Pengjiang Wang
ISPRS Int. J. Geo-Inf. 2026, 15(1), 7; https://doi.org/10.3390/ijgi15010007 - 21 Dec 2025
Viewed by 735
Abstract
To address the issue of decreased localization accuracy and robustness in existing visual SLAM systems caused by imprecise identification of dynamic regions in complex dynamic scenes—leading to dynamic interference or reduction in valid static feature points, this paper proposes a dynamic visual SLAM [...] Read more.
To address the issue of decreased localization accuracy and robustness in existing visual SLAM systems caused by imprecise identification of dynamic regions in complex dynamic scenes—leading to dynamic interference or reduction in valid static feature points, this paper proposes a dynamic visual SLAM method integrating instance-level motion classification, temporally adaptive super-pixel segmentation, and optical flow propagation. The system first employs an instance-level motion classifier combining residual flow estimation and a YOLOv8-seg instance segmentation model to distinguish moving objects. Then, temporally adaptive super-pixel segmentation algorithm SLIC (TA-SLIC) is applied to achieve fine-grained dynamic region partitioning. Subsequently, a proposed dynamic region missed-detection correction mechanism based on optical flow propagation (OFP) is used to refine the missed-detection mask, enabling accurate identification and capture of motion regions containing non-rigid local object movements, undefined moving objects, and low-dynamic objects. Finally, dynamic feature points are removed, and valid static features are utilized for pose estimation. The localization accuracy of the visual SLAM system is validated using two widely adopted datasets, TUM and BONN. Experimental results demonstrate that the proposed method effectively suppresses interference from dynamic objects (particularly non-rigid local motions) and significantly enhances both localization accuracy and system robustness in dynamic environments. Full article
(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)
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26 pages, 4176 KB  
Article
An Effective Approach to Geometric and Semantic BIM/GIS Data Integration for Urban Digital Twin
by Peyman Azari, Songnian Li and Ahmed Shaker
ISPRS Int. J. Geo-Inf. 2025, 14(12), 478; https://doi.org/10.3390/ijgi14120478 - 2 Dec 2025
Viewed by 1711
Abstract
Urban Digital Twins (UDTs) demand both simplified geometry and rich semantic information from Building Information Models (BIM) to be effectively integrated into Geospatial Information Systems (GIS). However, current BIM-to-GIS conversion methods struggle with geometric complexity and semantic loss, particularly at scale. This paper [...] Read more.
Urban Digital Twins (UDTs) demand both simplified geometry and rich semantic information from Building Information Models (BIM) to be effectively integrated into Geospatial Information Systems (GIS). However, current BIM-to-GIS conversion methods struggle with geometric complexity and semantic loss, particularly at scale. This paper proposes a novel, scalable methodology for comprehensive BIM/GIS integration, addressing both geometric and semantic challenges. The approach introduces a geometry conversion workflow that transforms solid BIMs into valid, simplified CityGML representations through a level-by-level detection of building elements and outer surface extraction. To preserve semantic richness, all entities, attributes, and relationships—including implicit connections—are automatically extracted and stored in a Labeled Property Graph (LPG) database. The method is further extended with a new CityGML Application Domain Extension (ADE) that supports Multi-LoD4 representations, enabling selective interior visualization and efficient rendering. A web-based urban digital twin platform demonstrates the integration, allowing dynamic semantic querying and scalable 3D visualization. Results show a significant reduction in geometric complexity, full semantic retention, and robust performance in visualization and querying, offering a practical pathway for advanced UDT development. Full article
(This article belongs to the Topic The Geography of Digital Twin: Concepts, Architectures, Modeling, AI and Applications)
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27 pages, 7255 KB  
Article
A Methodology to Convert Highly Detailed BIM Models into 3D Geospatial Building Models at Different LoDs
by Jasper van der Vaart, Ken Arroyo Ohori and Jantien Stoter
ISPRS Int. J. Geo-Inf. 2025, 14(12), 465; https://doi.org/10.3390/ijgi14120465 - 28 Nov 2025
Cited by 1 | Viewed by 828
Abstract
This paper presents an implemented methodology to convert highly detailed building information models (BIMs) into geospatial 3D city models (Geos) at multiple levels of detail (LoDs). As BIM models contain highly detailed and complex geometries that differ significantly from city model standards, abstraction [...] Read more.
This paper presents an implemented methodology to convert highly detailed building information models (BIMs) into geospatial 3D city models (Geos) at multiple levels of detail (LoDs). As BIM models contain highly detailed and complex geometries that differ significantly from city model standards, abstraction and conversion methods are required to generate usable outputs. Our study addresses this by developing a methodology that generates nine different LoDs from a single IFC input. These LoDs include both volumetric and surface-based abstractions for exterior and interior representations. The methodology involves voxelisation, filtering and simplification of surfaces, footprint derivation, storey abstraction, and interior geometry extraction. Together, these approaches allow flexible conversion tailored to specific applications, balancing accuracy, complexity, and computational efficiency. The methodology is implemented in a prototype tool named IfcEnvelopeExtractor. It automates IFC-to-CityGML/CityJSON conversion with minimal user input. The methodology was tested on a variety of models ranging from small houses to multistorey buildings. The evaluation covered geometric accuracy, semantic accuracy, and model complexity. Results show that non-volumetric abstractions and interior abstractions performed very well, producing robust and accurate results. However, the accuracy decreased for volumetric and complex abstractions, particularly at higher LoDs. Problems included missing or incorrectly trimmed surfaces, and modelling gaps and tolerance issues in the input IFC models. These limitations reveal that the quality of the input BIM models significantly affects the reliability of conversions. Overall, the methodology demonstrates that automated, flexible, and open-source solutions can effectively bridge the gap between BIM and geospatial domains, contributing to scalable GeoBIM integration in practice. Full article
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19 pages, 3176 KB  
Article
Collaborative Feminist Cartography in Geographical Education: Mapping Gender Representation in Street Naming (Las Calles de las Mujeres)
by María Sebastián López, Ondrej Kratochvíl, José Antonio Mérida Donoso, Juan Mar-Beguería and Rafael De Miguel González
ISPRS Int. J. Geo-Inf. 2025, 14(11), 440; https://doi.org/10.3390/ijgi14110440 - 7 Nov 2025
Viewed by 1583
Abstract
Collaborative mapping has emerged in recent decades as a key practice for producing open geospatial knowledge and fostering critical citizenship. However, several studies have shown that these platforms may reproduce existing gender inequalities, both in terms of participation and representation. This article examines [...] Read more.
Collaborative mapping has emerged in recent decades as a key practice for producing open geospatial knowledge and fostering critical citizenship. However, several studies have shown that these platforms may reproduce existing gender inequalities, both in terms of participation and representation. This article examines the potential of collaborative feminist cartography as a strategy for making inequalities visible and promoting gender equality in public space. Methodologically, the study focuses on the project Las Calles de las Mujeres, developed by Geochicas OSM, combining quantitative analysis of street naming in urban development with qualitative implementation in educational contexts. A global overview of 32 cities in 11 countries is provided, with a detailed case study of 11 Spanish cities. Results confirm the persistence of a significant gender gap in urban toponymy: streets named after men not only outnumber those dedicated to women but are also on average longer, more central, and symbolically more prominent. Educational experiences in Spain provide learning outcomes and demonstrate that collaborative mapping strengthens spatial thinking, digital competence, and critical awareness, linking geography education to the Sustainable Development Goals (SDG 5 and SDG 11). The article concludes that feminist mapping initiatives are simultaneously pedagogical, social, and political tools, capable of fostering more inclusive and sustainable cities. Full article
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23 pages, 6892 KB  
Article
Built-Up Surface Ensemble Model for Romania Based on OpenStreetMap, Microsoft Building Footprints, and Global Human Settlement Layer Data Sources Using Triple Collocation Analysis
by Zsolt Magyari-Sáska and Ionel Haidu
ISPRS Int. J. Geo-Inf. 2025, 14(11), 420; https://doi.org/10.3390/ijgi14110420 - 28 Oct 2025
Viewed by 1443
Abstract
Accurate and up-to-date data on built-up areas are crucial for urban planning, disaster management, and sustainable development, yet Romania still lacks a unified, official database. In this study we integrated the three widely used global data sources—OpenStreetMap (OSM), Microsoft Building Footprints (MSBFs), and [...] Read more.
Accurate and up-to-date data on built-up areas are crucial for urban planning, disaster management, and sustainable development, yet Romania still lacks a unified, official database. In this study we integrated the three widely used global data sources—OpenStreetMap (OSM), Microsoft Building Footprints (MSBFs), and Global Human Settlement Layer Built-up surface (GHS)—onto a 10 m resolution raster grid and applied this consistently at the national scale across 3181 settlement polygons to produce a more accurate, unified ensemble model for Romania. The methodological basis was Triple Collocation Analysis (TCA), extended with ETC/CTC to estimate per-settlement scale factors, enabling the quantification and optimal weighting of the relative errors and accuracy in the absence of independent reference data. Weight patterns vary by settlement type: OSM receives relatively higher weights in smaller rural settlements with less redundant error; in municipalities the stronger OSM–MSBF correlation reduces both of their weights and increases the GHS share; cities exhibit a more balanced weighting. At cell level, the ensemble provides uncertainty quantification via confidence intervals that typically range from 2% to 14% at settlement scale. The resulting model—like any model—does not perfectly reflect reality; however, the ensemble improves the accuracy and timeliness of the available data. The resulting model is replicable and updatable with newer data, making it suitable for numerous practical applications, especially in spatial development and risk analysis. Full article
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21 pages, 2429 KB  
Article
Visualizing Spatial Cognition for Wayfinding Design: Examining Gaze Behaviors Using Mobile Eye Tracking in Counseling Service Settings
by Jain Kwon, Alea Schmidt, Chenyi Luo, Eunwoo Jun and Karina Martinez
ISPRS Int. J. Geo-Inf. 2025, 14(10), 406; https://doi.org/10.3390/ijgi14100406 - 16 Oct 2025
Cited by 4 | Viewed by 3488
Abstract
Wayfinding with minimal effort is essential for reducing cognitive load and emotional stress in unfamiliar environments. This exploratory quasi-experimental study investigated wayfinding challenges in a university building housing three spatially dispersed counseling centers and three academic departments that share the building entrances, lobby, [...] Read more.
Wayfinding with minimal effort is essential for reducing cognitive load and emotional stress in unfamiliar environments. This exploratory quasi-experimental study investigated wayfinding challenges in a university building housing three spatially dispersed counseling centers and three academic departments that share the building entrances, lobby, and hallways. Using mobile eye tracking with concurrent think-aloud protocols and schematic mapping, we examined visual attention patterns during predefined navigation tasks performed by 24 first-time visitors. Findings revealed frequent fixations on non-informative structural features, while existing wayfinding cues were often overlooked. High rates of null gazes indicated unsuccessful visual searching. Thematic analysis of verbal data identified eight key issues, including spatial confusion, aesthetic monotony, and inadequate signage. Participants frequently described the environment as disorienting and emotionally taxing, comparing it to institutional settings such as hospitals. In response, we developed wayfinding design proposals informed by our research findings, stakeholder needs, and contextual priorities. We used an experiential digital twin that prioritized perceptual fidelity to analyze the current wayfinding challenges, develop experimental protocols, and discuss design options and costs. This study offers a transferable methodological framework for identifying wayfinding challenges through convergent analysis of gaze patterns and verbal protocols, demonstrating how empirical findings can inform targeted wayfinding design interventions. Full article
(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)
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28 pages, 38011 KB  
Article
On the Use of LLMs for GIS-Based Spatial Analysis
by Roberto Pierdicca, Nikhil Muralikrishna, Flavio Tonetto and Alessandro Ghianda
ISPRS Int. J. Geo-Inf. 2025, 14(10), 401; https://doi.org/10.3390/ijgi14100401 - 14 Oct 2025
Cited by 4 | Viewed by 5004
Abstract
This paper presents an approach integrating Large Language Models (LLMs), specifically GPT-4 and the open-source DeepSeek-R1, into Geographic Information System (GIS) workflows to enhance the accessibility, flexibility, and efficiency of spatial analysis tasks. We designed and implemented a system capable of interpreting natural [...] Read more.
This paper presents an approach integrating Large Language Models (LLMs), specifically GPT-4 and the open-source DeepSeek-R1, into Geographic Information System (GIS) workflows to enhance the accessibility, flexibility, and efficiency of spatial analysis tasks. We designed and implemented a system capable of interpreting natural language instructions provided by users and translating them into automated GIS workflows through dynamically generated Python scripts. An interactive graphical user interface (GUI), built using CustomTkinter, was developed to enable intuitive user interaction with GIS data and processes, reducing the need for advanced programming or technical expertise. We conducted an empirical evaluation of this approach through a comparative case study involving typical GIS tasks such as spatial data validation, data merging, buffer analysis, and thematic mapping using urban datasets from Pesaro, Italy. The performance of our automated system was directly compared against traditional manual workflows executed by 10 experienced GIS analysts. The results from this evaluation indicate a substantial reduction in task completion time, decreasing from approximately 1 h and 45 min in the manual approach to roughly 27 min using our LLM-driven automation, without compromising analytical quality or accuracy. Furthermore, we systematically evaluated the system’s factual reliability using a diverse set of geospatial queries, confirming robust performance for practical GIS tasks. Additionally, qualitative feedback emphasized improved usability and accessibility, particularly for users without specialized GIS training. These findings highlight the significant potential of integrating LLMs into GISs, demonstrating clear advantages in workflow automation, user-friendliness, and broader adoption of advanced spatial analysis methodologies. Full article
(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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17 pages, 6488 KB  
Article
A Spatial Analysis of the Association Between Urban Heat and Coronary Heart Disease
by Kyle Lucas, Ben Dewitt, Donald J. Biddle and Charlie H. Zhang
ISPRS Int. J. Geo-Inf. 2025, 14(9), 344; https://doi.org/10.3390/ijgi14090344 - 7 Sep 2025
Cited by 1 | Viewed by 1855
Abstract
Heart disease remains the leading cause of death in both the United States and globally. Urban heat is increasingly recognized as a significant public health challenge, particularly in its connection to cardiovascular conditions. This study, conducted in Jefferson County, Kentucky, examines the distribution [...] Read more.
Heart disease remains the leading cause of death in both the United States and globally. Urban heat is increasingly recognized as a significant public health challenge, particularly in its connection to cardiovascular conditions. This study, conducted in Jefferson County, Kentucky, examines the distribution of coronary heart disease rates and develops an urban heat risk index to examine underlying socioeconomic and environmental factors. We applied bivariate spatial association (Lee’s L), Global Moran’s I, and multiple linear regression methods to examine the relationships between key variables and assess model significance. Global Moran’s I revealed clustered distributions of both coronary heart disease rates and land surface temperature across census tracts. Bivariate spatial analysis identified clusters of high heart disease rates and temperatures within the West End, while clusters of contiguous suburban tracts exhibited lower heart disease rates and temperatures. Regression analyses yielded significant results for both the ordinary least squares (OLS) model and the spatial regression model; however, the spatial error model explained a greater proportion of the variation in coronary heart disease rates across tracts compared to the OLS model. This study offers new insights into spatial disparities in coronary heart disease rates and their associations with environmental risk factors including urban heat, underscoring the challenges faced by many urban communities. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))
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16 pages, 1329 KB  
Article
Vector Data Rendering Performance Analysis of Open-Source Web Mapping Libraries
by Dániel Balla and Mátyás Gede
ISPRS Int. J. Geo-Inf. 2025, 14(9), 336; https://doi.org/10.3390/ijgi14090336 - 30 Aug 2025
Cited by 4 | Viewed by 5481
Abstract
Nowadays, various technologies exist with differing rendering performance for interactive web maps. These maps are consumed on devices with varying capabilities; therefore, choosing the best-performing library for a dataset is emphasized. Unlike existing research, this study presents a comparative analysis on libraries’ native [...] Read more.
Nowadays, various technologies exist with differing rendering performance for interactive web maps. These maps are consumed on devices with varying capabilities; therefore, choosing the best-performing library for a dataset is emphasized. Unlike existing research, this study presents a comparative analysis on libraries’ native performance for rendering large amounts of GeoJSON vector data, partially extracted from OpenStreetMap (OSM). Four libraries were analyzed. Results showed that regardless of feature types, Leaflet and OpenLayers excelled for features up to 10,000. Up to 5000 points, these two were the fastest, above which the libraries’ performance converged. For 50,000 or more, Mapbox GL JS rendered them the quickest, followed by OpenLayers, MapLibre GL JS and Leaflet. For up to 50,000 lines and 10,000 polygons, Leaflet and OpenLayers were the fastest in all scenarios. For 100,000 lines, OpenLayers was almost twice as fast as the others, while Mapbox rendered 50,000 polygons the quickest. The performance of Leaflet and OpenLayers scales with the increasing feature quantities, yet for Mapbox and MapLibre, any performance impact is offset to 1000 features and beyond. Slow initalization of map elements makes Mapbox and MapLibre less suitable for rapid rendering of small feature quantities. Other behavioural differences affecting user experience are also explored. Full article
(This article belongs to the Special Issue Cartography and Geovisual Analytics)
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21 pages, 2655 KB  
Article
A Hybrid Approach for Geo-Referencing Tweets: Transformer Language Model Regression and Gazetteer Disambiguation
by Thomas Edwards, Padraig Corcoran and Christopher B. Jones
ISPRS Int. J. Geo-Inf. 2025, 14(9), 321; https://doi.org/10.3390/ijgi14090321 - 22 Aug 2025
Cited by 2 | Viewed by 1968
Abstract
Recent approaches to geo-referencing X posts have focused on the use of language modelling techniques that learn geographic region-specific language and use this to infer geographic coordinates from text. These approaches rely on large amounts of labelled data to build accurate predictive models. [...] Read more.
Recent approaches to geo-referencing X posts have focused on the use of language modelling techniques that learn geographic region-specific language and use this to infer geographic coordinates from text. These approaches rely on large amounts of labelled data to build accurate predictive models. However, obtaining significant volumes of geo-referenced data from Twitter, recently renamed X, can be difficult. Further, existing language modelling approaches can require the division of a given area into a grid or set of clusters, which can be dataset-specific and challenging for location prediction at a fine-grained level. Regression-based approaches in combination with deep learning address some of these challenges as they can assign coordinates directly without the need for clustering or grid-based methods. However, such approaches have received only limited attention for the geo-referencing task. In this paper, we adapt state-of-the-art neural network models for the regression task, focusing on geo-referencing wildlife Tweets where there is a limited amount of data. We experiment with different transfer learning techniques for improving the performance of the regression models, and we also compare our approach to recently developed Large Language Models and prompting techniques. We show that using a location names extraction method in combination with regression-based disambiguation, and purely regression when names are absent, leads to significant improvements in locational accuracy over using only regression. Full article
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21 pages, 2555 KB  
Article
Statistical Depth Measures in Density-Based Clustering with Automatic Adjustment for Skewed Data
by Mark McKenney and Daniel Tucek
ISPRS Int. J. Geo-Inf. 2025, 14(8), 298; https://doi.org/10.3390/ijgi14080298 - 30 Jul 2025
Viewed by 1216
Abstract
Statistical data depth measures have been applied to density-based clustering techniques in an effort to achieve robustness in parameter selection via the affine invariant property of the depth measure. Specifically, the Mahalanobis depth measure is used in the application of DBSCAN. In this [...] Read more.
Statistical data depth measures have been applied to density-based clustering techniques in an effort to achieve robustness in parameter selection via the affine invariant property of the depth measure. Specifically, the Mahalanobis depth measure is used in the application of DBSCAN. In this paper, we examine properties of the Mahalanobis depth measure that lead to instances where it fails to detect clusters in DBSCAN, whereas Euclidean distance is able to differentiate the clusters. We propose two solutions to the problems induced by these properties. The first re-examines clusters to determine if data shape is causing multiple clusters to be grouped into a single cluster. The second examines the use of a different measure as an alternate depth function. Experiments are provided. Full article
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25 pages, 3204 KB  
Article
Assessing Spatial Digital Twins for Oil and Gas Projects: An Informed Argument Approach Using ISO/IEC 25010 Model
by Sijan Bhandari and Dev Raj Paudyal
ISPRS Int. J. Geo-Inf. 2025, 14(8), 294; https://doi.org/10.3390/ijgi14080294 - 28 Jul 2025
Viewed by 2533
Abstract
With the emergence of Survey 4.0, the oil and gas (O & G) industry is now considering spatial digital twins during their field design to enhance visualization, efficiency, and safety. O & G companies have already initiated investments in the research and development [...] Read more.
With the emergence of Survey 4.0, the oil and gas (O & G) industry is now considering spatial digital twins during their field design to enhance visualization, efficiency, and safety. O & G companies have already initiated investments in the research and development of spatial digital twins to build digital mining models. Existing studies commonly adopt surveys and case studies as their evaluation approach to validate the feasibility of spatial digital twins and related technologies. However, this approach requires high costs and resources. To address this gap, this study explores the feasibility of the informed argument method within the design science framework. A land survey data model (LSDM)-based digital twin prototype for O & G field design, along with 3D spatial datasets located in Lot 2 on RP108045 at petroleum lease 229 under the Department of Resources, Queensland Government, Australia, was selected as a case for this study. The ISO/IEC 25010 model was adopted as a methodology for this study to evaluate the prototype and Digital Twin Victoria (DTV). It encompasses eight metrics, such as functional suitability, performance efficiency, compatibility, usability, security, reliability, maintainability, and portability. The results generated from this study indicate that the prototype encompasses a standard level of all parameters in the ISO/IEC 25010 model. The key significance of the study is its methodological contribution to evaluating the spatial digital twin models through cost-effective means, particularly under circumstances with strict regulatory requirements and low information accessibility. Full article
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22 pages, 5960 KB  
Article
Application of Integrated Geospatial Analysis and Machine Learning in Identifying Factors Affecting Ride-Sharing Before/After the COVID-19 Pandemic
by Afshin Allahyari and Farideddin Peiravian
ISPRS Int. J. Geo-Inf. 2025, 14(8), 291; https://doi.org/10.3390/ijgi14080291 - 28 Jul 2025
Viewed by 1567
Abstract
Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after [...] Read more.
Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after a significant delay following the lockdown. This raises the question of what determinants shape ride-pooling in the post-pandemic era and how they spatially influence shared ride-hailing compared to the pre-pandemic period. To address this gap, this study employs geospatial analysis and machine learning to examine the factors affecting ride-pooling trips in pre- and post-pandemic periods. Using over 66 million trip records from 2019 and 43 million from 2023, we observe a significant decline in shared trip adoption, from 16% to 2.91%. The results of an extreme gradient boosting (XGBoost) model indicate a robust capture of non-linear relationships. The SHAP analysis reveals that the percentage of the non-white population is the dominant predictor in both years, although its influence weakened post-pandemic, with a breakpoint shift from 78% to 90%, suggesting reduced sharing in mid-range minority areas. Crime density and lower car ownership consistently correlate with higher sharing rates, while dense, transit-rich areas exhibit diminished reliance on shared trips. Our findings underscore the critical need to enhance transportation integration in underserved communities. Concurrently, they highlight the importance of encouraging shared ride adoption in well-served, high-demand areas where solo ride-hailing is prevalent. We believe these results can directly inform policies that foster more equitable, cost-effective, and sustainable shared mobility systems in the post-pandemic landscape. Full article
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23 pages, 4020 KB  
Article
Exploring Unconventional 3D Geovisualization Methods for Land Suitability Assessment: A Case Study of Jihlava City
by Oldrich Bittner, Jakub Zejdlik, Jaroslav Burian and Vit Vozenilek
ISPRS Int. J. Geo-Inf. 2025, 14(7), 269; https://doi.org/10.3390/ijgi14070269 - 8 Jul 2025
Cited by 2 | Viewed by 2379
Abstract
Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability [...] Read more.
Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability for residential development in Jihlava, Czechia. Using five raster-based data layers derived from a multi-criteria evaluation (Urban Planner methodology) across three time horizons (2023, 2028, 2033), the visualizations were implemented in ArcGIS Online and assessed by 19 domain experts via a structured questionnaire. The evaluation focused on clarity, usability, and accuracy in interpreting land suitability values, with the methods being rated on a five-point scale. Results show that the Horizontal Planes method was rated highest in terms of interpretability and user satisfaction, while 3D Surface and Vertical Planes were considered the least effective. The study demonstrates that visualization methods employing visual variables (e.g., color and transparency) are better suited for land suitability communication. The methodological contribution lies in systematically comparing 3D visualization techniques for thematic spatial data, providing guidance for their application in planning practice. The results are primarily intended for urban planners, designers, and local government representatives as supportive tools for efficient planning of future built-up area development. Full article
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21 pages, 3075 KB  
Article
Evaluating Real-Time and Scheduled Public Transport Data: Challenges and Opportunities
by Liam Webb, Gary Higgs, Mitchel Langford and Robert Berry
ISPRS Int. J. Geo-Inf. 2025, 14(7), 243; https://doi.org/10.3390/ijgi14070243 - 25 Jun 2025
Cited by 2 | Viewed by 6087
Abstract
Scheduled timetable information has been used extensively in studies concerned with estimating travel times in accessibility research. Fewer studies to date have involved the use of real-time public transport data to help investigate the impacts of travel disruptions or cancellations of service on [...] Read more.
Scheduled timetable information has been used extensively in studies concerned with estimating travel times in accessibility research. Fewer studies to date have involved the use of real-time public transport data to help investigate the impacts of travel disruptions or cancellations of service on reported spatial and temporal patterns of accessibility. The aims of this paper are to introduce, describe, and compare the salient features and relative merits of alternative data sources relating to real-time transport data that could be utilized in such applications. By drawing attention to the potential of real-time data originating from such sources, this study makes recommendations for those considering building on the use of scheduled data to incorporate travel time reliability within transport applications. We conclude by highlighting the need for further research that explores the potential of using openly available sources of real-time traffic data in studies that incorporate accessibility analysis. Full article
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15 pages, 3095 KB  
Article
A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island
by Pavlos Fylaktos, George P. Petropoulos and Ioannis Lemesios
ISPRS Int. J. Geo-Inf. 2025, 14(6), 220; https://doi.org/10.3390/ijgi14060220 - 2 Jun 2025
Cited by 4 | Viewed by 2368
Abstract
The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. This study explores the innovative application of the so-called Mask Region-based convolutional neural network (Mask R-CNN) algorithm in a GIS environment, [...] Read more.
The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. This study explores the innovative application of the so-called Mask Region-based convolutional neural network (Mask R-CNN) algorithm in a GIS environment, utilizing high-resolution satellite imagery from the WorldView-3 system. By combining these state-of-the-art technologies, this study demonstrates the algorithm’s effectiveness at recognizing and segmenting the ancient structures within the archaeological site of Delos, Greece. Despite the computational constraints, the outcomes are promising, with around 25.91% of the initial vector data (434 out of 1675 polygons) successfully identified. The algorithm achieved an impressive F1 Score of 0.93% at a threshold of 0.9, indicating its high precision in differentiating specific features from their environments. This research highlights AI’s crucial role in archaeology, enabling the remote analysis of vast areas through automated or semi-automated techniques. Although these technologies cannot supplant essential on-site investigations, they can significantly enhance traditional methodologies by minimizing costs and fieldwork duration. This study also points out obstacles, such as the complexity of and variability in archaeological remains, which complicate the creation of standardized data libraries. Nevertheless, as AI technologies progress, their applications in archaeology are anticipated to broaden, fostering further innovation within the discipline. Full article
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21 pages, 6514 KB  
Article
Evacuation Behavioural Instructions with 3D Motions: Insights from Three Use Cases
by Ruihang Xie, Sisi Zlatanova, Jinwoo (Brian) Lee and André Borrmann
ISPRS Int. J. Geo-Inf. 2025, 14(5), 197; https://doi.org/10.3390/ijgi14050197 - 8 May 2025
Cited by 2 | Viewed by 3040
Abstract
During emergency evacuations, pedestrians may use three-dimensional (3D) motions, such as low crawling and climbing up/down, to navigate above or below indoor objects (e.g., tables, chairs, and stair flights). Understanding how these motions influence evacuation processes can facilitate the development of behavioural instructions. [...] Read more.
During emergency evacuations, pedestrians may use three-dimensional (3D) motions, such as low crawling and climbing up/down, to navigate above or below indoor objects (e.g., tables, chairs, and stair flights). Understanding how these motions influence evacuation processes can facilitate the development of behavioural instructions. This study examines the influence of 3D motions through a simulation-based method. This method combines a voxel-based 3D indoor model with an agent-based model. Three use case studies are elaborated upon, considering varying building types, agent numbers, urgency levels, and demographic differences. These case studies serve as exploratory demonstrations rather than validated simulations grounded in real-world evacuation experiments. Our findings are as follows: (1) Three-dimensional motions may create alternative and local 3D paths, enabling agents to bypass congestion, particularly in narrow corridors and confined spaces. (2) While 3D motions may help alleviate local congestion, they may intensify bottlenecks near exits, especially in highly crowded and high-urgency scenarios. (3) As urgency and agent numbers increase, differences in evacuation efficiency between scenarios with and without 3D motions are likely to diminish. We suggest further investigation into evacuation behavioural instructions, including the following: (1) conditional use of 3D motions in different buildings and (2) instructions tailored to different demographic groups. These use cases illustrate new directions for evacuation managers to consider the incorporation of 3D motions. Full article
(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)
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23 pages, 2596 KB  
Article
RouteLAND: An Integrated Method and a Geoprocessing Tool for Characterizing the Dynamic Visual Landscape Along Highways
by Loukas-Moysis Misthos and Vassilios Krassanakis
ISPRS Int. J. Geo-Inf. 2025, 14(5), 187; https://doi.org/10.3390/ijgi14050187 - 30 Apr 2025
Cited by 1 | Viewed by 2089
Abstract
Moving away from a static concept for the landscape that surrounds us, in this research article, we approach the visual landscape as a dynamic concept. Moreover, we attempt to provide an interconnection between the domains of landscape and cartography by designing maps that [...] Read more.
Moving away from a static concept for the landscape that surrounds us, in this research article, we approach the visual landscape as a dynamic concept. Moreover, we attempt to provide an interconnection between the domains of landscape and cartography by designing maps that are particularly suitable for characterizing the visible landscape and are potentially meaningful for overall landscape evaluation. Thus, the present work mainly focuses on the consecutive computation of vistas along highways, incorporating actual landscape composition—as the landscape is perceived from an egocentric perspective by observers moving along highway routes in peri-urban landscapes. To this end, we developed an integrated method and a Python (version 2.7.16) tool, named “RouteLAND”, for implementing an algorithmic geoprocessing procedure; through this geoprocessing tool, sequences of composite dynamic geospatial analyses and geometric calculations are automatically implemented. The final outputs are interactive web maps, whereby the segments of highway routes are characterized according to the dominant element of the visible landscape by employing (spatial) aggregation techniques. The developed geoprocessing tool and the generated interactive map provide a cartographic exploratory tool for summarizing the landscape character of highways in any peri-urban landscape, while hypothetically moving in a vehicle. In addition, RouteLAND can potentially aid in the assessment of existing or future highways’ scenic level and in the sustainable design of new highways based on the minimization of intrusive artificial structures’ vistas; in this sense, RouteLAND can serve as a valuable tool for landscape evaluation and sustainable spatial planning and development. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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29 pages, 74025 KB  
Article
Geospatial Framework for Assessing the Suitability and Demand for Agricultural Digital Solutions in Europe: A Tool for Informed Decision-Making
by Theodoros Chalazas, Antonis Koukourikos, Jan Bauwens, Nick Berkvens, Jonathan Van Beek, Nikos Kalatzis, George Papadopoulos, Panagiotis Ilias, Nikolaos Marianos and Christopher Brewster
ISPRS Int. J. Geo-Inf. 2025, 14(5), 185; https://doi.org/10.3390/ijgi14050185 - 25 Apr 2025
Cited by 1 | Viewed by 2917
Abstract
This study introduces a geospatial comprehensive methodological system aimed at evaluating the suitability and need for agricultural digital solutions (ADSs) across Europe. This system integrates a diverse range of factors, including geophysical characteristics, climate patterns, and socioeconomic conditions, evaluated at regional- and farm-specific [...] Read more.
This study introduces a geospatial comprehensive methodological system aimed at evaluating the suitability and need for agricultural digital solutions (ADSs) across Europe. This system integrates a diverse range of factors, including geophysical characteristics, climate patterns, and socioeconomic conditions, evaluated at regional- and farm-specific levels. By leveraging open-source Earth observations and socioeconomic data, we develop multiple performance, environmental, and socioeconomic similarity indexes that compare regions based on shared characteristics, such as soil quality, climate, and socioeconomic factors. Using advanced statistical and multi-criteria analysis tools, these indexes are tailored to different stages of agricultural production, enabling region-specific assessments that identify and prioritize the needs for digital solutions across Europe. The results indicate that the developed indexes effectively categorize regions based on comparable characteristics, facilitating the targeted recommendation of ADSs. Additionally, a connectivity performance index is created to assess the local deployment model of agricultural digital solutions (cloud, edge, or mixed), ensuring that the recommendations for technological implementation are feasible and effective given the local connectivity conditions. Full article
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20 pages, 5374 KB  
Article
The Urban–Rural Education Divide: A GIS-Based Assessment of the Spatial Accessibility of High Schools in Romania
by Angelo Andi Petre, Liliana Dumitrache, Alina Mareci and Alexandra Cioclu
ISPRS Int. J. Geo-Inf. 2025, 14(5), 183; https://doi.org/10.3390/ijgi14050183 - 24 Apr 2025
Cited by 6 | Viewed by 6422
Abstract
Educational achievement plays a significant role in the labour market, benefiting individuals and society. Graduating from high school is a key step towards better employment opportunities and a prerequisite for higher education attainment. In 2023, only 22.5% of the Romanian population graduated tertiary [...] Read more.
Educational achievement plays a significant role in the labour market, benefiting individuals and society. Graduating from high school is a key step towards better employment opportunities and a prerequisite for higher education attainment. In 2023, only 22.5% of the Romanian population graduated tertiary education, while 16.6% left education or training early. The Romanian public high school network comprises 1558 units, mostly located in urban areas. The high school enrolment rate is 83.5% in urban areas, and it drops to less than 60% in rural areas, with the country registering the highest out-of-school rate in the EU for the 15-year-old population. Spatial accessibility may influence enrolment in high schools, particularly for students living in rural or remote areas, who often face financial challenges fuelled by long distances and limited transportation options. Hence, travel distance may represent a potential barrier to completing the educational process or may determine inequalities in educational opportunities and outcomes. This paper aims to assess the spatial accessibility of the public high school network in Romania by using distance data provided by the Open Street Map API (Application Programming Interface). We examine variations in spatial accessibility based on the distribution of high school units and road network characteristics considering three variables: travel distance to the nearest high school, the average distance to three different categories of high schools, and the number of high schools located within a 20 km buffer zone. The results highlight a significant urban–rural divide in the availability of public high school facilities, with 84.1% (n = 1311) located in urban areas while 49.1% of the high school-aged population lives in rural areas. Many rural communities lack adequate educational facilities, often having limited options for high school education. The findings also show that 32% of the high school-aged population has to travel more than 10 km to the nearest high school, and 7% has no high school options within a 20 km buffer zone. This study provides insights into the educational landscape in Romania, pointing out areas with limited access to high schools, which contributes to further inequalities in educational attainment. The findings may serve as a basis for developing policies and practices to bridge the urban–rural divide in educational opportunities and foster a more equitable and inclusive education system. Full article
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39 pages, 7188 KB  
Review
Georeferencing Building Information Models for BIM/GIS Integration: A Review of Methods and Tools
by Peyman Azari, Songnian Li, Ahmed Shaker and Shahram Sattar
ISPRS Int. J. Geo-Inf. 2025, 14(5), 180; https://doi.org/10.3390/ijgi14050180 - 22 Apr 2025
Cited by 9 | Viewed by 6645
Abstract
With the rise of urban digital twins and smart cities, the integration of building information modeling (BIM) and geospatial information systems (GISs) have captured the interest of researchers. Although significant advancements have been achieved in this field, challenges persist in the georeferencing of [...] Read more.
With the rise of urban digital twins and smart cities, the integration of building information modeling (BIM) and geospatial information systems (GISs) have captured the interest of researchers. Although significant advancements have been achieved in this field, challenges persist in the georeferencing of BIM models, which is one of the fundamental challenges in integrating BIM and GIS models. These challenges stem from dissimilarities between the BIM and GIS domains, including different georeferencing definitions, different coordinate systems utilization, and a lack of correspondence between the engineering system of BIM and the project’s geographical location. This review critically examines the significance of georeferencing within this integration, outlines and compares various methods for georeferencing BIM data in detail, and surveys existing software tools that facilitate this process. The findings underscore the need for increased attention to georeferencing issues from both domains, aiming to enhance the seamless integration of BIM and GIS. Full article
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19 pages, 2850 KB  
Article
Use and Effectiveness of Chatbots as Support Tools in GIS Programming Course Assignments
by Hartwig H. Hochmair
ISPRS Int. J. Geo-Inf. 2025, 14(4), 156; https://doi.org/10.3390/ijgi14040156 - 2 Apr 2025
Cited by 4 | Viewed by 5768
Abstract
Advancements in large language models have significantly transformed higher education by integrating AI chatbots into course design, teaching, administration, and student support. This study evaluates the use, effectiveness, and perceptions of chatbots in a Python-based graduate-level GIS programming course at a U.S. university. [...] Read more.
Advancements in large language models have significantly transformed higher education by integrating AI chatbots into course design, teaching, administration, and student support. This study evaluates the use, effectiveness, and perceptions of chatbots in a Python-based graduate-level GIS programming course at a U.S. university. Students self-reported perceived improvements in skills and the use of different help resources across three home assignments of varying complexity and spatial context. In group discussions, students shared their experiences, strategies, and envisioned future applications of chatbots in GIS programming and beyond. The results indicate that prior programming experience enhances students’ perception of assignment usefulness, and that chatbots serve as a partial replacement for traditional help resources (e.g., websites) in completing assignments. Overall, students expressed positive sentiments regarding chatbot effectiveness, especially for complex spatial tasks. While students were optimistic about the potential of chatbots to enhance future learning, concerns were raised about overreliance on AI, which could hinder the development of independent problem-solving and programming skills. In conclusion, this study offers valuable insights into optimizing chatbot integration in GIS education. Full article
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26 pages, 6562 KB  
Article
A Model of Building Changes to Support Comparative Studies and Open Discussions on Densification
by Bénédicte Bucher, Juste Raimbault, Mouhamadou Ndim, Ana-Maria Raimond, Julien Perret, Sebastian Dembski and Mathias Jehling
ISPRS Int. J. Geo-Inf. 2025, 14(4), 155; https://doi.org/10.3390/ijgi14040155 - 2 Apr 2025
Cited by 1 | Viewed by 1816
Abstract
Densification is a widely used concept, but there is a lack of terminology and tools to facilitate discussions among data scientists, policy makers and citizens. This paper proposes a model of building changes based on building surveys undertaken in past decades to connect [...] Read more.
Densification is a widely used concept, but there is a lack of terminology and tools to facilitate discussions among data scientists, policy makers and citizens. This paper proposes a model of building changes based on building surveys undertaken in past decades to connect discussions about densification with shared evidence. A specific challenge is to process buildings in city regions and areas in a replicable way across different building data sources. Another challenge is to manage the quality of the representation, i.e., how well the maps represent changes to buildings and how well they can support discussions of densification. Building data and real buildings are different things that sometimes change in an independent way. Addressing these factors requires different forms of expertise, i.e., expertise about the realities depicted in the areas studied, about local data sources, and about advanced matching tools and state-of-the-art densification concepts. We present a collaborative dashboard through which to engage corresponding experts in the production of building change maps and the clarification of related concepts. Full article
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27 pages, 2788 KB  
Article
Critical Success Factors of Participatory Community Planning with Geospatial Digital Participatory Platforms
by Karl Atzmanstorfer, Mona Bartling, Barbora Haltofová, Leo Zurita-Arthos, Judith Grubinger-Preiner and Anton Eitzinger
ISPRS Int. J. Geo-Inf. 2025, 14(4), 153; https://doi.org/10.3390/ijgi14040153 - 1 Apr 2025
Cited by 3 | Viewed by 3407
Abstract
In recent years, Digital Participatory Platforms (DPPs) have become an increasingly popular tool for citizen participation in community planning processes. They serve municipalities, citizen initiatives, and other planning authorities as digital tools to collect feedback, discuss ideas, solve problems and monitor small-scale planning [...] Read more.
In recent years, Digital Participatory Platforms (DPPs) have become an increasingly popular tool for citizen participation in community planning processes. They serve municipalities, citizen initiatives, and other planning authorities as digital tools to collect feedback, discuss ideas, solve problems and monitor small-scale planning processes within their communities. In addition, DPPs facilitate the integration of the spatial domain into participatory community planning. In this paper, we assess the most important Critical Success Factors (CSFs) of participatory community planning with geospatial DPPs, and analyze the potential, opportunities, and challenges associated with integrating these platforms into community planning. We analyze the results of a digital questionnaire that we shared with a selected group of expert scholars and community stakeholders. We then contextualize this feedback with our experiences from the piloting phase and commercial roll-out of the ‘Bürgercockpit’-application for participatory community planning within the Austrian Agenda21-framework. As a result, we identify the most important CSFs of participatory community planning with geospatial DPPs. This set of CSFs should provide a better orientation on how to complement well-established analog participatory methods and practices with geospatial DPPs for the co-production of shared visions and solutions, ultimately empowering all stakeholders of a planning process to better manage their communities. Full article
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19 pages, 10779 KB  
Article
Conceptual Neighborhood Graphs of Topological Relations in Z2
by Brendan Patrick Hall and Matthew Paul Dube
ISPRS Int. J. Geo-Inf. 2025, 14(4), 150; https://doi.org/10.3390/ijgi14040150 - 31 Mar 2025
Cited by 2 | Viewed by 1626
Abstract
Topological relations form the backbone of qualitative spatial reasoning and, as such, play a paramount role in geographic information systems. Three decades of research have provided a proliferation of sets of qualitative topological relations in both continuous and discretized spaces, but only in [...] Read more.
Topological relations form the backbone of qualitative spatial reasoning and, as such, play a paramount role in geographic information systems. Three decades of research have provided a proliferation of sets of qualitative topological relations in both continuous and discretized spaces, but only in continuous spaces has the concept of organizing these relations into a larger framework (called a conceptual neighborhood graph) been considered. Previous work leveraged matrix differences to derive the anisotropic scaling neighborhood for these relations. In this paper, a simulation protocol is used to derive conceptual neighborhood graphs of qualitative topological relations in Z2 for the operations of translation and isotropic scaling. It is further shown that, when aggregating raster relations into their continuous counterparts and collapsing neighborhood connections within these groups, the familiar conceptual neighborhood structures for continuous regions appear. Full article
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30 pages, 16455 KB  
Article
Automated Detection of Pedestrian and Bicycle Lanes from High-Resolution Aerial Images by Integrating Image Processing and Artificial Intelligence (AI) Techniques
by Richard Boadu Antwi, Prince Lartey Lawson, Michael Kimollo, Eren Erman Ozguven, Ren Moses, Maxim A. Dulebenets and Thobias Sando
ISPRS Int. J. Geo-Inf. 2025, 14(4), 135; https://doi.org/10.3390/ijgi14040135 - 23 Mar 2025
Cited by 4 | Viewed by 3065
Abstract
The rapid advancement of computer vision technology is transforming how transportation agencies collect roadway characteristics inventory (RCI) data, yielding substantial savings in resources and time. Traditionally, capturing roadway data through image processing was seen as both difficult and error-prone. However, considering the recent [...] Read more.
The rapid advancement of computer vision technology is transforming how transportation agencies collect roadway characteristics inventory (RCI) data, yielding substantial savings in resources and time. Traditionally, capturing roadway data through image processing was seen as both difficult and error-prone. However, considering the recent improvements in computational power and image recognition techniques, there are now reliable methods to identify and map various roadway elements from multiple imagery sources. Notably, comprehensive geospatial data for pedestrian and bicycle lanes are still lacking across many state and local roadways, including those in the State of Florida, despite the essential role this information plays in optimizing traffic efficiency and reducing crashes. Developing fast, efficient methods to gather this data are essential for transportation agencies as they also support objectives like identifying outdated or obscured markings, analyzing pedestrian and bicycle lane placements relative to crosswalks, turning lanes, and school zones, and assessing crash patterns in the associated areas. This study introduces an innovative approach using deep neural network models in image processing and computer vision to detect and extract pedestrian and bicycle lane features from very high-resolution aerial imagery, with a focus on public roadways in Florida. Using YOLOv5 and MTRE-based deep learning models, this study extracts and segments bicycle and pedestrian features from high-resolution aerial images, creating a geospatial inventory of these roadway features. Detected features were post-processed and compared with ground truth data to evaluate performance. When tested against ground truth data from Leon County, Florida, the models demonstrated accuracy rates of 73% for pedestrian lanes and 89% for bicycle lanes. This initiative is vital for transportation agencies, enhancing infrastructure management by enabling timely identification of aging or obscured lane markings, which are crucial for maintaining safe transportation networks. Full article
(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)
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37 pages, 7441 KB  
Review
Hexahedral Projections: A Comprehensive Review and Ranking
by Aleksandar Dimitrijević and Peter Strobl
ISPRS Int. J. Geo-Inf. 2025, 14(3), 122; https://doi.org/10.3390/ijgi14030122 - 6 Mar 2025
Viewed by 2930
Abstract
Hexahedral projections—mapping the Earth’s surface onto the faces of a circumscribed cube—have drawn scientific interest for over half a century. During this time, numerous projections with diverse characteristics have been developed. This paper provides the most comprehensive review of these projections to date, [...] Read more.
Hexahedral projections—mapping the Earth’s surface onto the faces of a circumscribed cube—have drawn scientific interest for over half a century. During this time, numerous projections with diverse characteristics have been developed. This paper provides the most comprehensive review of these projections to date, offering a detailed examination of the processes involved in projecting the Earth onto a cube, with a focus on distortion and accuracy. A numerical and graphical analysis of the characteristics of hexahedral projections is presented, serving as the foundation for a composite hierarchical metric based on ranking. This metric is used to rank hexahedral projections according to individual criteria, groups of criteria, and overall performance. Full article
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20 pages, 17194 KB  
Article
Understanding the Carbon Footprint of Tile Transfer for Web Maps
by Guillaume Touya, Azelle Courtial, Jérémy Kalsron, Justin Berli, Bérénice Le Mao and Laura Wenclik
ISPRS Int. J. Geo-Inf. 2025, 14(3), 107; https://doi.org/10.3390/ijgi14030107 - 1 Mar 2025
Cited by 3 | Viewed by 1660
Abstract
As web maps are now extensively used by billions of users, the energy consumption of these maps is not marginal anymore. Green cartography seeks to reduce the energy consumption of maps to promote more sustainable digital tools. To reduce energy consumption, we first [...] Read more.
As web maps are now extensively used by billions of users, the energy consumption of these maps is not marginal anymore. Green cartography seeks to reduce the energy consumption of maps to promote more sustainable digital tools. To reduce energy consumption, we first need to better understand the different sources of energy consumption for web maps. Among these sources, this paper focuses on the tiles that are stored on servers and then constantly transferred each time a user explores the map. This paper presents several experiments carried out with current web maps to assess this energy consumption. We first try to assess the number of map tiles that are loaded through the web when users explore web maps, and we determine which types of interaction are used with the maps, and a similar amount of tiles is loaded. Then, we try to assess which zoom levels are the most loaded by users; it appears that the medium–large scales are the most used (between zoom levels 11 and 17). Then, we explore the size of the map tiles and try to assess which ones are larger and thus require more energy to load over the web; we can find clear differences between zoom levels. Finally, we discuss how map generalization could be used to reduce energy consumption by creating lighter tiles. These experiments show that the current web maps are suboptimal regarding energy consumption, with many tiles loaded at zoom levels where the tiles are larger than necessary. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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23 pages, 9017 KB  
Article
Climate Change Maps for the Atlas of Switzerland
by Luca Gaia, Andreas Neumann and Lorenz Hurni
ISPRS Int. J. Geo-Inf. 2025, 14(3), 99; https://doi.org/10.3390/ijgi14030099 - 22 Feb 2025
Cited by 1 | Viewed by 3475
Abstract
Climate change has global consequences, and Switzerland is no exception. The communication of climate change poses various challenges, and maps are often part of this process. This work presents three maps illustrating the impacts of climate change, developed for the Atlas of Switzerland [...] Read more.
Climate change has global consequences, and Switzerland is no exception. The communication of climate change poses various challenges, and maps are often part of this process. This work presents three maps illustrating the impacts of climate change, developed for the Atlas of Switzerland (AoS), an interactive digital national atlas. The aim is to make climate change impacts understandable and visible. Three different indicators of climate change were visualized: the rise of the zero degree line, the evolution of glacial lakes, and changes in the flowering dates of plants. Various approaches were employed that leverage the strengths of the AoS, including temporal navigation, interactivity, 3D data visualizations, and map combinations. The feasibility of these visualizations are demonstrated through the presented maps and analysis of key considerations for their creation. We believe these map types should be included in national atlases and can contribute to the achievement of Sustainable Development Goal 13: “Climate Action”. Further research is needed to assess the effectiveness and user understandability of the proposed maps. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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16 pages, 1482 KB  
Article
Mobile Cadastral Application with Open-Source Software in Colombia
by Gaspar Mora-Navarro, Carmen Femenia-Ribera, Enric Terol and Cristhian Quiza-Neuto
ISPRS Int. J. Geo-Inf. 2025, 14(3), 96; https://doi.org/10.3390/ijgi14030096 - 20 Feb 2025
Cited by 2 | Viewed by 3262
Abstract
This article presents social research, conducted through interviews with experts involved in land administration in Colombia, on the possibility of using the Fit-For-Purpose methodology, combined with indirect methods, to accelerate the capture of cadastral data. The experts were asked about the design of [...] Read more.
This article presents social research, conducted through interviews with experts involved in land administration in Colombia, on the possibility of using the Fit-For-Purpose methodology, combined with indirect methods, to accelerate the capture of cadastral data. The experts were asked about the design of a data capture system, using a mobile application, to acquire data on properties and their approximate coordinates, as well as the data of their owners, where the owners themselves are the ones who declare these data. A functional prototype has also been developed and tested in Spain. Results: The design is well received, understood as a declaration by owners, especially in rural areas; further processing of the information by technicians of the competent authority is necessary; involving the population has a positive impact on the perception that owners have regarding cadastral processes; some technical and training challenges must be taken into account, to ensure consistency and quality in the data collected; and the prototype tests demonstrate, due to the low GPS accuracy of mobile phones, that the identification of boundaries over a base map is possible in properties of one hectare or more. Full article
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19 pages, 22324 KB  
Article
Beyond the Road: A Regional Perspective on Traffic Congestion in Metro Atlanta
by Jeong Chang Seong, Seungyeon Lee, Yoonjae Cho and Chulsue Hwang
ISPRS Int. J. Geo-Inf. 2025, 14(2), 61; https://doi.org/10.3390/ijgi14020061 - 3 Feb 2025
Cited by 1 | Viewed by 6876
Abstract
Traffic congestion not only affects traffic flow but also influences public perception of congested regions. While analyzing congestion at the road section level can help identify engineering solutions, it often fails to reveal broader spatial patterns and trends at the regional or macro [...] Read more.
Traffic congestion not only affects traffic flow but also influences public perception of congested regions. While analyzing congestion at the road section level can help identify engineering solutions, it often fails to reveal broader spatial patterns and trends at the regional or macro scale unless summarized effectively. This study aims to address these challenges by focusing on regional-scale traffic congestion amounts measured by distanceTime metrics. A 12–month dataset, sampled every 10 min, was analyzed to identify spatial patterns, temporal trends, regional variations, and predictive models in the Metro Atlanta area. The results show that congestion is the most severe and increasing at key urban corridors like Brookhaven–Sandy Springs, the downtown connector, Druid Hills–Decatur, and Johns Creek–Cumming, aligning with recent urban developments. Cities such as Alpharetta, Dunwoody, Brookhaven, Austell, Stone Mountain, East Point, Lake City, Morrow, Fairburn, and Jonesboro show high increasing trends in congestion. Predictive modeling with the long short-term memory (LSTM) method shows promising results for short-term forecasts, though variability in data requires further optimization for certain cities. This research is significant because it demonstrates that congestion amounts measured by distanceTime metrics can be used for assessing regional characteristics broadly at a metropolitan city scale. The findings and methodologies identified in this research might support urban and transportation planning efforts in metropolitan planning organizations, such as the Atlanta Regional Commission, by identifying congestion amounts and trends at both the regional and road scales. Full article
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23 pages, 19140 KB  
Article
Enhancing Spatial Awareness and Collaboration: A Guide to VR-Ready Survey Data Transformation
by Joseph Kevin McDuff, Armin Agha Karimi and Zahra Gharineiat
ISPRS Int. J. Geo-Inf. 2025, 14(2), 59; https://doi.org/10.3390/ijgi14020059 - 2 Feb 2025
Cited by 2 | Viewed by 2776
Abstract
Surveying and spatial science are experiencing a paradigm shift from traditional data outputs to more immersive and interactive formats, driven by the rise in Virtual Reality (VR). This study addresses the challenge of transforming UAV (Unmanned Aerial Vehicle)-acquired photogrammetry data into VR-compatible surfaces [...] Read more.
Surveying and spatial science are experiencing a paradigm shift from traditional data outputs to more immersive and interactive formats, driven by the rise in Virtual Reality (VR). This study addresses the challenge of transforming UAV (Unmanned Aerial Vehicle)-acquired photogrammetry data into VR-compatible surfaces while preserving the accuracy and quality crucial to professional surveying. The study leverages Blender, an open-source 3D creation tool, to develop a procedural guide for creating VR-ready models from high-quality survey data. The case study focuses on silos located in Yelarbon, Southeast Queensland, Australia. UAV mapping is utilised to gather the data necessary for 3D modelling with a few minor alterations in the photo capturing angle and processing. Key findings reveal that while Blender excels as a visualisation tool, it struggles with geospatial precision, particularly when handling large numbers coming from coordinate systems, leading to rounding errors seen within the VR model. Blender’s strength lies in creating immersive experiences for public engagement but is constrained by its lack of capability to hold survey metadata, hindering its applicability for professional survey-grade outputs. The results highlight the need for further development into possible Blender plugins that integrate geospatial accuracy with VR outputs. This study underscores the potential of VR to enhance how survey data are visualised, offering opportunities for future innovations in both the technical and creative aspects of the surveying profession. Full article
(This article belongs to the Topic Technological Innovation and Emerging Operational Applications in Digital Earth)
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25 pages, 17627 KB  
Article
The Machine Learning-Based Mapping of Urban Pluvial Flood Susceptibility in Seoul Integrating Flood Conditioning Factors and Drainage-Related Data
by Julieber T. Bersabe and Byong-Woon Jun
ISPRS Int. J. Geo-Inf. 2025, 14(2), 57; https://doi.org/10.3390/ijgi14020057 - 1 Feb 2025
Cited by 7 | Viewed by 7787
Abstract
In the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-conditioning factors [...] Read more.
In the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-conditioning factors is vital for identifying flood-prone areas and developing predictive models in highly urbanized regions. This study evaluates and maps urban pluvial flood susceptibility in Seoul, South Korea using the machine learning techniques such as logistic regression (LR), random forest (RF), and support vector machines (SVM), and integrating traditional flood conditioning factors and drainage-related data. Together with known flooding points from 2010 to 2022, sixteen flood conditioning factors were selected, including the drainage-related parameters sewer pipe density (SPD) and distance to a storm drain (DSD). The RF model performed best (accuracy: 0.837, an area under the receiver operating characteristic curve (AUC): 0.902), and indicated that 32.65% of the study area has a high susceptibility to flooding. The accuracy and AUC were improved by 7.58% and 3.80%, respectively, after including the two drainage-related variables in the model. This research provides valuable insights for urban flood management, highlighting the primary causes of flooding in Seoul and identifying areas with heightened flood susceptibility, particularly relating to drainage infrastructure. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)
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33 pages, 10796 KB  
Article
Use of Semantic Web Technologies to Enhance the Integration and Interoperability of Environmental Geospatial Data: A Framework Based on Ontology-Based Data Access
by Sajith Ranatunga, Rune Strand Ødegård, Knut Jetlund and Erling Onstein
ISPRS Int. J. Geo-Inf. 2025, 14(2), 52; https://doi.org/10.3390/ijgi14020052 - 28 Jan 2025
Cited by 9 | Viewed by 5222
Abstract
This study addresses the challenges of integrating heterogeneous environmental geospatial data by proposing a framework based on ontology-based data access (OBDA). Geospatial data are important for decision-making in various domains, such as environmental monitoring, disaster management, and urban development. Data integration is a [...] Read more.
This study addresses the challenges of integrating heterogeneous environmental geospatial data by proposing a framework based on ontology-based data access (OBDA). Geospatial data are important for decision-making in various domains, such as environmental monitoring, disaster management, and urban development. Data integration is a common challenge within these domains due to data heterogeneity and semantic discrepancies. The proposed framework uses semantic web technologies to enhance data interoperability, accessibility, and usability. Several practical examples were demonstrated to validate its effectiveness. These examples were based in Lake Mjøsa, Norway, addressing both spatial and non-spatial scenarios to test the framework’s potential. By extending the GeoSPARQL ontology, the framework supports SPARQL queries to retrieve information based on user requirements. A web-based SPARQL Query Interface (SQI) was developed to execute queries and display the retrieved data in tabular and visual format. Utilizing free and open-source software (FOSS), the framework is easily replicable for stakeholders and researchers. Despite some limitations, the study concludes that the framework is able to enhance cross-domain data integration and semantic querying in various informed decision-making scenarios. Full article
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19 pages, 7846 KB  
Article
A GIS-Based Framework to Analyze the Behavior of Urban Greenery During Heatwaves Using Satellite Data
by Barbara Cardone, Ferdinando Di Martino, Cristiano Mauriello and Vittorio Miraglia
ISPRS Int. J. Geo-Inf. 2024, 13(11), 377; https://doi.org/10.3390/ijgi13110377 - 30 Oct 2024
Cited by 4 | Viewed by 2907
Abstract
This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land [...] Read more.
This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST). The aim of this research is to analyze the behavior of urban vegetation types during heatwaves through the analysis of these three indices. To evaluate how these indices characterize urban green areas during heatwaves, an unsupervised classification method of the three indices is proposed that uses the Elbow method to determine the optimal number of classes and the Jenks classification algorithm. Each class is assigned a Gaussian fuzzy set and the green urban areas are classified using zonal statistics operators. The membership degree of the corresponding fuzzy set is calculated to assess the reliability of the classification. Finally, for each type of greenery, the frequencies of types of green areas belonging to NDVI, NDMI, and LST classes are analyzed to evaluate their behavior during heatwaves. The framework was tested in an urban area consisting of the city of Naples (Italy). The results show that some types of greenery, such as deciduous forests and olive groves, are more efficient, in terms of health status and cooling effect, than other types of urban green areas during heatwaves; they are classified with NDVI and NDMI values of mainly High and Medium High, and maximum LST values of Medium Low. Conversely, uncultivated areas show critical behaviors during heatwaves; they are classified with maximum NDVI and NDMI values of Medium Low and maximum LST values of Medium High. The research results represent a support to urban planners and local municipalities in designing effective strategies and nature-based solutions to deal with heat waves in urban settlements. Full article
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29 pages, 38136 KB  
Article
Constructing Efficient Mesh-Based Global Grid Systems with Reduced Distortions
by Lakin Wecker, John Hall and Faramarz F. Samavati
ISPRS Int. J. Geo-Inf. 2024, 13(11), 373; https://doi.org/10.3390/ijgi13110373 - 22 Oct 2024
Cited by 2 | Viewed by 3224
Abstract
Recent advancements in geospatial technologies have significantly expanded the volume and diversity of geospatial data, unlocking new and innovative applications that require novel Geographic Information Systems (GIS). (Discrete) Global Grid Systems (DGGSs) have emerged as a promising solution to further enhance modern geospatial [...] Read more.
Recent advancements in geospatial technologies have significantly expanded the volume and diversity of geospatial data, unlocking new and innovative applications that require novel Geographic Information Systems (GIS). (Discrete) Global Grid Systems (DGGSs) have emerged as a promising solution to further enhance modern geospatial capabilities. Current DGGSs employ a simple, low-resolution polyhedral approximation of the Earth for efficient operations, but require a projection between the Earth’s surface and the polyhedral faces. Equal-area DGGSs are desirable for their low distortion, but they fall short of this promise due to the inefficiency of equal-area projections. On the other hand, efficiency-first DGGSs need to better address distortion. We introduce a novel mesh-based DGGS (MBD) which generalizes efficient operations over watertight triangular meshes with spherical topology. Unlike traditional approaches that rely on Platonic or Catalan solids, our mesh-based method leverages high-resolution spherical meshes to offer greater flexibility and accuracy. MBD allows high-resolution polyhedra (HRP) to be used as the base polyhedron of a DGGS, significantly reducing distortion. To address the operational challenges, we introduce a new hash encoding method and an efficient barycentric indexing method (BIM). MBD extends Atlas of Connectivity Maps to the BIM to provide efficient spatial and hierarchical traversal. We introduce several new base polyhedra with lower areal and angular distortion, and we experimentally validate their properties and demonstrate their efficiency. Our experimentation shows that we achieve constant-time operations for high-resolution MBD, and we recommend polyhedra to be used as the base polyhedron for low-distortion DGGSs, compact faces, and efficient operations. Full article
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36 pages, 13506 KB  
Article
ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models
by Ali Mansourian and Rachid Oucheikh
ISPRS Int. J. Geo-Inf. 2024, 13(10), 348; https://doi.org/10.3390/ijgi13100348 - 1 Oct 2024
Cited by 46 | Viewed by 18855
Abstract
Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to [...] Read more.
Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis. Full article
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24 pages, 210054 KB  
Article
Scale- and Resolution-Adapted Shaded Relief Generation Using U-Net
by Marianna Farmakis-Serebryakova, Magnus Heitzler and Lorenz Hurni
ISPRS Int. J. Geo-Inf. 2024, 13(9), 326; https://doi.org/10.3390/ijgi13090326 - 12 Sep 2024
Cited by 1 | Viewed by 2496
Abstract
On many maps, relief shading is one of the most significant graphical elements. Modern relief shading techniques include neural networks. To generate such shading automatically at an arbitrary scale, one needs to consider how the resolution of the input digital elevation model (DEM) [...] Read more.
On many maps, relief shading is one of the most significant graphical elements. Modern relief shading techniques include neural networks. To generate such shading automatically at an arbitrary scale, one needs to consider how the resolution of the input digital elevation model (DEM) relates to the neural network process and the maps used for training. Currently, there is no clear guidance on which DEM resolution to use to generate relief shading at specific scales. To address this gap, we trained the U-Net models on swisstopo manual relief shadings of Switzerland at four different scales and using four different resolutions of SwissALTI3D DEM. An interactive web application designed for this study allows users to outline a random area and compare histograms of varying brightness between predictions and manual relief shadings. The results showed that DEM resolution and output scale influence the appearance of the relief shading, with an overall scale/resolution ratio. We present guidelines for generating relief shading with neural networks for arbitrary areas and scales. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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28 pages, 37910 KB  
Article
Cultural Heritage in Times of Crisis: Damage Assessment in Urban Areas of Ukraine Using Sentinel-1 SAR Data
by Ute Bachmann-Gigl and Zahra Dabiri
ISPRS Int. J. Geo-Inf. 2024, 13(9), 319; https://doi.org/10.3390/ijgi13090319 - 5 Sep 2024
Cited by 6 | Viewed by 4907
Abstract
Cultural property includes immovable assets that are part of a nation’s cultural heritage and reflect the cultural identity of a people. Hence, information about armed conflict’s impact on historical buildings’ structures and heritage sites is extremely important. The study aims to demonstrate the [...] Read more.
Cultural property includes immovable assets that are part of a nation’s cultural heritage and reflect the cultural identity of a people. Hence, information about armed conflict’s impact on historical buildings’ structures and heritage sites is extremely important. The study aims to demonstrate the application of Earth observation (EO) synthetic aperture radar (SAR) technology, and in particular Sentinel-1 SAR coherence time-series analysis, to monitor spatial and temporal changes related to the recent Russian–Ukrainian war in the urban areas of Mariupol and Kharkiv, Ukraine. The study considers key events during the siege of Mariupol and the battle of Kharkiv from February to May 2022. Built-up areas and cultural property were identified using freely available OpenStreetMap (OSM) data. Semi-automated coherent change-detection technique (CCD) that utilize difference analysis of pre- and co-conflict coherences were capable of highlighting areas of major impact on the urban structures. The study applied a logistic regression model (LRM) for the discrimination of damaged and undamaged buildings based on an estimated likelihood of damage occurrence. A good agreement was observed with the reference data provided by the United Nations Satellite Centre (UNOSAT) in terms of the overall extent of damage. Damage maps enable the localization of buildings and cultural assets in areas with a high probability of damage and can serve as the basis for a high-resolution follow-up investigation. The study reveals the benefits of Sentinel-1 SAR CCD in the sense of unsupervised delineation of areas affected by armed conflict. However, limitations arise in the detection of local and single-building damage compared to regions with large-scale destruction. The proposed semi-automated multi-temporal Sentinel-1 data analysis using CCD methodology shows its applicability for the timely investigation of damage to buildings and cultural heritage, which can support the response to crises. Full article
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17 pages, 7654 KB  
Article
The Impact of Airbnb on Long-Term Rental Markets in San Francisco: A Geospatial Analysis Using Multiscale Geographically Weighted Regression
by Dongkeun Hur, Seonjin Lee and Hany Kim
ISPRS Int. J. Geo-Inf. 2024, 13(9), 298; https://doi.org/10.3390/ijgi13090298 - 23 Aug 2024
Viewed by 5668
Abstract
The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap [...] Read more.
The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap in understanding whether Airbnb financially burdens local renters within different income groups. The authors also differentiated the effect of Airbnb accommodations with different levels of commercialization by categorizing Airbnb listings based on their level of commercialization. Using the multiscale geographically weighted regression technique, this study also considered spatial variations in the relationship between short- and long-term rental markets. The findings indicate that the density of Airbnb only affects the relative rent of renters with a yearly household income between USD 50,000 and USD 75,000. Furthermore, the density of Airbnb listings from more commercialized hosts that own between three and eleven showed a positive relationship with the relative rent cost. This study highlighted the variability in the impact of Airbnb on the local community by income group, listing characteristic, and geographic region. This finding underscores the need for differentiated regulation toward peer-to-peer accommodations, as the impact on rent affordability varies by host commercialization level and renter income group. Full article
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27 pages, 20774 KB  
Article
Genetic Programming to Optimize 3D Trajectories
by André Kotze, Moritz Jan Hildemann, Vítor Santos and Carlos Granell
ISPRS Int. J. Geo-Inf. 2024, 13(8), 295; https://doi.org/10.3390/ijgi13080295 - 20 Aug 2024
Viewed by 3286
Abstract
Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal [...] Read more.
Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimize the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimization with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimization by developing a novel GP algorithm to optimize trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimization problems in science and engineering. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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26 pages, 9857 KB  
Article
Spatiotemporal Analysis of Nighttime Crimes in Vienna, Austria
by Jiyoung Lee, Michael Leitner and Gernot Paulus
ISPRS Int. J. Geo-Inf. 2024, 13(7), 247; https://doi.org/10.3390/ijgi13070247 - 10 Jul 2024
Cited by 6 | Viewed by 9139
Abstract
Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during [...] Read more.
Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during the nighttime, investigating the temporal definition of nighttime crime and the correlation between nighttime lights and criminal activities. The study concentrates on four types of nighttime crimes, assault, theft, burglary, and robbery, conducting univariate and multivariate analyses. In the univariate analysis, correlations between nighttime crimes and nighttime light (NTL) values detected in satellite images and between streetlight density and nighttime crimes are explored. The results highlight that nighttime burglary strongly relates to NTL and streetlight density. The multivariate analysis delves into the relationships between each nighttime crime type and socioeconomic and urban infrastructure variables. Once again, nighttime burglary exhibits the highest correlation. For both univariate and multivariate regression models the geographically weighted regression (GWR) outperforms ordinary least squares (OLS) regression in explaining the relationships. This study underscores the importance of considering the location and offense time in crime geography research and emphasizes the potential of using NTL in nighttime crime analysis. Full article
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20 pages, 8876 KB  
Article
A Comprehensive Survey on High-Definition Map Generation and Maintenance
by Kaleab Taye Asrat and Hyung-Ju Cho
ISPRS Int. J. Geo-Inf. 2024, 13(7), 232; https://doi.org/10.3390/ijgi13070232 - 1 Jul 2024
Cited by 12 | Viewed by 10498
Abstract
The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully [...] Read more.
The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully automated vehicles in the foreseeable future. High-definition (HD) maps are central to this endeavor, offering centimeter-level accuracy in mapping the environment and enabling precise localization. Unlike conventional maps, these highly detailed HD maps are critical for autonomous vehicle decision-making, ensuring safe and accurate navigation. Compiled before testing and regularly updated, HD maps meticulously capture environmental data through various methods. This study explores the vital role of HD maps in autonomous driving, delving into their creation, updating processes, and the challenges and future directions in this rapidly evolving field. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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19 pages, 6750 KB  
Article
A Sensor-Based Simulation Method for Spatiotemporal Event Detection
by Yuqin Jiang, Andrey A. Popov, Zhenlong Li, Michael E. Hodgson and Binghu Huang
ISPRS Int. J. Geo-Inf. 2024, 13(5), 141; https://doi.org/10.3390/ijgi13050141 - 23 Apr 2024
Cited by 2 | Viewed by 2825
Abstract
Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the [...] Read more.
Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as “sensors”, which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur. Full article
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